如何在 OpenCV python 中从图片中检测对象（血液）

Question

我只想检测图片中的物体 原图： 背景图像：

import cv2
import numpy as np
import os

def detect_blood(input_path):

    img = cv2.imread(input_path)
    imgBlurred = cv2.GaussianBlur(img, (5,5), 0)
    gray = cv2.cvtColor(imgBlurred, cv2.COLOR_BGR2GRAY)

    ret2, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
    kernel = np.ones((5, 5), np.uint8)
    mask = cv2.erode(thresh, kernel, iterations=2)
    mask = cv2.dilate(mask, kernel, iterations=2)
    foreground = cv2.bitwise_and(img, img, mask=mask)

    # Find contours in the mask
    min_area = 1000  # Adjust this value as needed
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    large_contours = [c for c in contours if cv2.contourArea(c) > min_area]
    
    # Draw the contours on the original image
    cv2.drawContours(foreground, large_contours, -1, (0, 255, 0), 3)

    # Resize the image to a smaller size
    resized_img = cv2.resize(img, (480, 640))# Resize the image to a smaller size
    resized_mask = cv2.resize(mask, (480, 640))
    resized_foreground = cv2.resize(foreground, (480, 640))

    # cv2.imshow("Original", resized_img)
    cv2.imshow("mask", resized_mask)
    cv2.imshow("output", resized_foreground)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    return foreground

def process_images(input_folder, output_folder):
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)

    for filename in os.listdir(input_folder):
        if filename.endswith(('.jpg', '.jpeg', '.png')):
            input_path = os.path.join(input_folder, filename) # "./Blood/blood(1).jpg"
            output_path = os.path.join(output_folder, 'result_{}'.format(filename)) # ./img_detect/result_

            result = detect_blood(input_path)
            cv2.imwrite(output_path, result)

if __name__ == "__main__":
    input_folder = "./BloodV2"
    output_folder = "./img_detect"

    process_images(input_folder, output_folder)

面膜： 结果： 现在我无法检测 BG 和 Object 之间的区别

Answer 1

我尝试通过使用红色过滤来获取血液，并通过轮廓区域消除噪声。下面是我使用的代码，并在评论中进行了解释。希望这有帮助

import cv2
import numpy as np

image = cv2.imread('bld.jpg') #Read input image
result = image.copy()

#Convert to HSV and filter image using red as blood will be red
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([155,25,0])
upper = np.array([179,255,255])
mask = cv2.inRange(image, lower, upper)
mask = cv2.erode(mask, None, iterations=2)  # Erode to remove noise
mask = cv2.dilate(mask, None, iterations=2)  # dilate to close gaps
#Find contour
contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # get contour

#Remove small contours to avoid noises
selected_contours = []
for ct in contours:
    if cv2.contourArea(ct) > 10000:  # Select contours greater than a particular area value.
        selected_contours.append(ct)
contour_image = np.zeros_like(mask)

#Get the mask by drawing selected contour
cv2.drawContours(contour_image, selected_contours, -1, (255), thickness=cv2.FILLED)
result = cv2.bitwise_and(result, result, mask=contour_image)
#Draw blood only image.
cv2.imwrite("bld_out.jpg",result)

输出图像：